A novel system for automated metadata generation of learning and knowledge Objects are presented. Such system automates the processes of adding descriptive and contextual information to digital learning content, digital documents, and other objects used in learning and knowledge management. It also automates the process of creating associations among higher level objects and classifications systems used to organize content, and it does so in a way that improves the functionality of existing technologies, that can be tuned to meet the needs of a particular organization or community of practice, and that can be extended and refined to take advantage of new information retrieval technologies. It includes methods that handle aggregate digital objects composed of a plurality of other objects and that improves efficiency by caching data and recognizing the relationships among aggregate objects and their components.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A system for automatic generation of metadata for learning and knowledge objects, said system comprising: a host application; a service layer; an analysis engine; a configuration module; and a data storage; wherein said configuration module is connected to and communicates with said service layer and said analysis engine; wherein said data storage is connected to said analysis engine and said host application; wherein said service layer is connected to and communicates with said analysis engine and said host application; wherein said service layer receives requests from and sends reply to said host application; wherein said service layer sends requests to and receives reply from said analysis engine; wherein said host application sends a first request for first metadata for an object along with information about said object to said service layer; wherein said object comprises one or more sub-objects; wherein said host application sends a second request for classification of said object according to one or more taxonomies to said service layer; wherein said service layer passes said first request, said second request, and said information to said analysis engine; and wherein said analysis engine processes said first request, said second request, said information and contextual information to generate said first metadata and returns said first metadata to said service layer, wherein said service layer passes said first metadata received from said analysis layer to said host application.
2. A system as recited in claim 1 , wherein said analysis engine comprising: a metadata generator; an information aggregation heuristics unit; an information retrieval harness; and a database; wherein said analysis engine identifies said object; wherein said analysis engine identifies said first metadata; wherein said analysis engine identifies said one or more taxonomies; wherein said analysis engine identifies information aggregation heuristics; wherein said analysis engine identifies one or more information retrieval routines that are used; wherein said analysis engine identifies contextual information that is used; wherein said analysis engine identifies third metadata for said one or more sub-objects that are previously stored in said database; wherein said information retrieval harness collects data from a plurality of said one or more information retrieval routines; wherein said information retrieval harness passes collected said data to said information aggregation heuristics unit; wherein said information aggregation heuristics unit processes said data and sends the processed data to said metadata generator; wherein said metadata generator generates said second metadata according to configured or stored taxonomies, metadata schema, organizational policies, and semantic spaces, and sends it to said service layer; and wherein said configuration module stores said one or more information retrieval routines, said taxonomies, said organizational policies, said metadata schema, and said semantic spaces used by said analysis engine and said information retrieval routines.
3. A system as stated in claim 2 , wherein said analysis engine comprising of an object aggregator and disaggregator; wherein said object aggregator and disaggregator aggregates and disaggregates said object to said one or more sub-objects, respectively; wherein said object aggregator and disaggregator stores said first metadata for said object and said one or more sub-objects in said database; wherein said object aggregator and disaggregator retrieves previously analyzed and stored information of said one or more sub-objects; wherein said object aggregator and disaggregator sends said previously analyzed and stored information to said information retrieval harness; wherein said information retrieval harness passes said previously analyzed and stored information to said information aggregation heuristics unit; wherein said information aggregation heuristics unit uses said previously analyzed and stored information and said data to compute said processed data.
4. A system as stated in claim 2 , wherein said host application requests from said analysis engine that said first metadata be compared and analyzed for consistency with automatically generated said second metadata.
5. A system as stated in claim 4 , wherein said analysis engine identifies the quality and comparison metrics for consistency analysis.
6. A system as stated in claim 2 , wherein services are offered based on said configuration module, wherein said services generate and tune said semantic spaces, define and update said taxonomies, create new metadata elements, or define or re-define said information aggregation heuristics for customers.
7. A system as stated in claim 2 , where said one or more information retrieval routines are substituted with new or improved information retrieval routines.
8. A system as stated in claim 2 , wherein said system is integrated directly into end said host application using integrated services.
9. A system as stated in claim 2 , wherein said system creates association among two or more said objects.
10. A system as stated in claim 2 , wherein said system adds descriptive and contextual information to digital learning content, digital documents, or other objects used in learning and knowledge management.
11. A system as stated in claim 2 , wherein said object is documents, Web page, assessments, knowledge objects, rights licenses, learning objects, competencies, learning interventions, or classifications systems used to organize content.
12. A system as stated in claim 2 , wherein said host application is document authoring tool, Web content authoring tool, learning content management system, rights management system, knowledge management tool, email applications, or software that manages content development workflows.
13. A system as stated in claim 2 , wherein said system caches data and recognizes the relationships among said objects and said one or more sub-objects.
14. A system as stated in claim 2 , wherein said system requests are triggered by user actions, including importing or publishing content.
15. A system as stated in claim 2 , wherein said system uses content aggregation de facto standards; wherein said standards comprise one or more of the following: MPEG-21 Part II, IMS Content Packaging, Darwin Information Typing Architecture, or Metadata Encoding and Transmission Standard.
16. A system as stated in claim 15 , wherein said content aggregation de facto standards uses Extensible Markup Language XML, or the Open Knowledge Initiative Service Interface Definitions JSR170.
17. A system as stated in claim 2 , wherein said service layer is a software service that provides application programmer interfaces or Web services that are described by Web Service Description Language and accessed through HTTP using the Simple Object Access Protocol.
18. A system as stated in claim 2 , wherein said host application is a learning content authoring tool and said metadata in conformant to the 1484.12.3-2005 IEEE Standard for Learning Technology, Extensible Markup Language (XML) Schema Definition Language Binding for Learning Object Metadata.
19. A system as stated in claim 2 , wherein said taxonomies include corporate structure or departments, taxonomies of skills, knowledge, abilities and tasks, and designations of confidentiality, classification, or usage rights.
20. A system as stated in claim 2 , wherein the user of said host application selects one or more classifications from a drop down list corresponding to said one or more taxonomies; said analysis engine returns its opinion of the correct classification together with a correlation between the selected classification and the suggested classification, based on semantic analysis techniques including Latent Semantic Analysis, and wherein said host application relies on said analysis engine to suggest a classification, and either accepts that suggestion or enable the user to view and modify that suggestion.
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October 19, 2008
March 20, 2012
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